imputeTS: Time Series Missing Value Imputation in R

The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact, that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a short overview about univariate time series imputation in R.

Steffen Moritz , Thomas Bartz-Beielstein

CRAN packages used

AMELIA, mice, VIM, missMDA, imputeTS, zoo, forecast, spacetime, timeSeries, xts

CRAN Task Views implied by cited packages

TimeSeries, Finance, Econometrics, OfficialStatistics, Environmetrics, Multivariate, SocialSciences, SpatioTemporal, Psychometrics, Spatial


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Moritz & Bartz-Beielstein, "The R Journal: imputeTS: Time Series Missing Value Imputation in R", The R Journal, 2017

BibTeX citation

  author = {Moritz, Steffen and Bartz-Beielstein, Thomas},
  title = {The R Journal: imputeTS: Time Series Missing Value Imputation in R},
  journal = {The R Journal},
  year = {2017},
  note = {},
  doi = {10.32614/RJ-2017-009},
  volume = {9},
  issue = {1},
  issn = {2073-4859},
  pages = {207-218}